{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets/\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "Training...\n",
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "100/100 [==============================] - 8s 81ms/step - loss: 1.2384 - acc: 0.7000 - val_loss: 1.2854 - val_acc: 0.8040 - time: 8.0815\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 413ms/step - test_loss: 1.4326 - test_acc: 0.8350 - time: 0.4130\n",
      "Test loss 1.4326, Test accuracy 83.50%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import GAT\n",
    "model = GAT(graph, device='GPU', attr_transform=\"normalize_attr\", seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"gat\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_attention (GraphAttention (None, 64)           91904       attr_matrix[0][0]                \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64)           0           graph_attention[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "graph_attention_1 (GraphAttenti (None, 7)            469         dropout[0][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "node_index (InputLayer)         [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gather (Gather)                 (None, 7)            0           graph_attention_1[0][0]          \n",
      "                                                                 node_index[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 92,373\n",
      "Trainable params: 92,373\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
